A systematic risk assessment approach to develop a fuzzy bow-tie model for third-party collaboration supporting circular economy: Application to electronic industry

Abstract

Reverse Logistics (RL) plays a pivotal role in the Indian electronics industry as it enables products to be recovered, refurbished, recycled, and disposed of in an eco-friendly manner thus gradually bringing the sector to circular economy (CE) practices. In order to manage these intricate and resource-demanding RL processes effectively, manufacturers increasingly collaborate with third-party reverse logistics providers (3PRLPs). On the contrary, such partnerships are naturally subjected to numerous risks due to the inefficiencies in operations, the differences in behaviours, and the strategic uncertainties. Despite previous research having highlighted various risk factors in RL, there remains a distinct and unfilled gap when it comes to the systematic identification and interlinking of these risks with the corresponding mitigation barriers and the potential consequences of collaboration failure, especially in the Indian electronic industry. In order to fill this gap, the current study utilizes a fuzzy bow-tie (BT) model to identify, scrutinize, and map the key risks associated with 3PRLP collaboration to preventive and mitigative barriers along with the cascading consequences of failure. The novelty of this study is in the thorough and detailed mapping of the cause-barrier-consequence relationships which provides a complete and profound understanding of risk propagation in reverse supply chain (RSC) partnerships. The fuzzy BT model is particularly suitable for this purpose as it manages the uncertainty and subjectivity that come with expert-based evaluations while enabling quantitative estimation of risk probabilities. The findings of the study highlight the pressing necessity for proactive and organized risk governance mechanisms to ensure continuous and resilient partnerships in RL. The analysis shows that the absence of timely risk management measures causes 6% probability of failure during collaboration with 3PRLPs. Moreover, if this failure takes place, there is a strong possibility of minor operational hindrances, and lack of responsiveness from RSC actors; a moderate possibility of productivity and revenue loss, and loss of customers and reputation; and a low possibility of major RL disruptions or total financial burden on the manufacturer. Hence, decision-support framework developed in the study can be an effective tool for SC practitioners for strengthening the risk resilience of RL collaboration with 3PRLPs and attainment of sustainability and CE goals.